Currently, the user of a medical imaging quality assurance (QA) software system is required to explicitly specify a type of QA phantom used to generate the image being loaded into the software system before the analysis of the image can begin. Additionally, a novice user may not know, or may be unsure, of the proper form of analysis to be applied to an image. Thus, a user must often select a form of analysis is to be conducted on an image from a large list of possibilities. While a user may have some foreknowledge of image phantom type, this knowledge is not relevant at the initial stage of analysis beyond choosing what type of analysis is to be performed. Therefore, the requirement to choose the phantom type limits totally automated systems where many different image types may be present.
Unfortunately, current systems require the user to identify a phantom type and do not allow for quickly and efficiently conduct medical imaging quality assurance by effectively removing the requirement to identify a phantom type; current systems do not allow the user to move directly from loading an image to obtaining analysis results. Further, current systems have limited if any capabilities for automated analysis of multiple images in batch form.